TBNilles/LEANN
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'main' into debug/clean-state-investigation

Browse Source
This commit is contained in:
Andy Lee
2025-08-11 01:56:38 -07:00
committed by GitHub
parent 8f3cda2100 239e35e2e6
commit 3806f2a3ba
17 changed files with 4385 additions and 3595 deletions
Download Patch File Download Diff File Expand all files Collapse all files

78
packages/leann-core/src/leann/mcp.py
View File

@@ -25,32 +25,61 @@ def handle_request(request):
"tools": [
{
"name": "leann_search",
"description": "Search LEANN index",
"description": """🔍 Search code using natural language - like having a coding assistant who knows your entire codebase!
🎯 **Perfect for**:
- "How does authentication work?" → finds auth-related code
- "Error handling patterns" → locates try-catch blocks and error logic
- "Database connection setup" → finds DB initialization code
- "API endpoint definitions" → locates route handlers
- "Configuration management" → finds config files and usage
💡 **Pro tip**: Use this before making any changes to understand existing patterns and conventions.""",
"inputSchema": {
"type": "object",
"properties": {
"index_name": {"type": "string"},
"query": {"type": "string"},
"top_k": {"type": "integer", "default": 5},
"index_name": {
"type": "string",
"description": "Name of the LEANN index to search. Use 'leann_list' first to see available indexes.",
},
"query": {
"type": "string",
"description": "Search query - can be natural language (e.g., 'how to handle errors') or technical terms (e.g., 'async function definition')",
},
"top_k": {
"type": "integer",
"default": 5,
"minimum": 1,
"maximum": 20,
"description": "Number of search results to return. Use 5-10 for focused results, 15-20 for comprehensive exploration.",
},
"complexity": {
"type": "integer",
"default": 32,
"minimum": 16,
"maximum": 128,
"description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
},
},
"required": ["index_name", "query"],
},
},
{
"name": "leann_ask",
"description": "Ask question using LEANN RAG",
"name": "leann_status",
"description": "📊 Check the health and stats of your code indexes - like a medical checkup for your codebase knowledge!",
"inputSchema": {
"type": "object",
"properties": {
"index_name": {"type": "string"},
"question": {"type": "string"},
"index_name": {
"type": "string",
"description": "Optional: Name of specific index to check. If not provided, shows status of all indexes.",
}
},
"required": ["index_name", "question"],
},
},
{
"name": "leann_list",
"description": "List all LEANN indexes",
"description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
"inputSchema": {"type": "object", "properties": {}},
},
]
@@ -63,19 +92,40 @@ def handle_request(request):
try:
if tool_name == "leann_search":
# Validate required parameters
if not args.get("index_name") or not args.get("query"):
return {
"jsonrpc": "2.0",
"id": request.get("id"),
"result": {
"content": [
{
"type": "text",
"text": "Error: Both index_name and query are required",
}
]
},
}
# Build simplified command
cmd = [
"leann",
"search",
args["index_name"],
args["query"],
"--recompute-embeddings",
f"--top-k={args.get('top_k', 5)}",
f"--complexity={args.get('complexity', 32)}",
]
result = subprocess.run(cmd, capture_output=True, text=True)
elif tool_name == "leann_ask":
cmd = f'echo "{args["question"]}" | leann ask {args["index_name"]} --recompute-embeddings --llm ollama --model qwen3:8b'
result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
elif tool_name == "leann_status":
if args.get("index_name"):
# Check specific index status - for now, we'll use leann list and filter
result = subprocess.run(["leann", "list"], capture_output=True, text=True)
# We could enhance this to show more detailed status per index
else:
# Show all indexes status
result = subprocess.run(["leann", "list"], capture_output=True, text=True)
elif tool_name == "leann_list":
result = subprocess.run(["leann", "list"], capture_output=True, text=True)